Top companies trust Airbyte to centralize their Data
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Set up a source connector to extract data from in Airbyte
Choose from one of 300+ sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code connector builder.
Configure the connection in Airbyte
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
Airbyte Open Source
Airbyte Cloud
Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
Fnatic, based out of London, is the world's leading esports organization, with a winning legacy of 16 years and counting in over 28 different titles, generating over 13m USD in prize money. Fnatic has an engaged follower base of 14m across their social media platforms and hundreds of millions of people watch their teams compete in League of Legends, CS:GO, Dota 2, Rainbow Six Siege, and many more titles every year.
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FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
1. Email delivery status: The Postmarkapp API allows you to extract data on the delivery status of emails sent through the platform. This includes information on whether an email was successfully delivered, bounced, or marked as spam.
2. Email open and click rates: The API also provides data on the number of times an email was opened and the number of clicks on links within the email.
3. Email content: You can extract the content of emails sent through Postmarkapp's API, including the subject line, body text, and any attachments.
4. Email recipient information: The API provides data on the email addresses of recipients, as well as any custom metadata associated with each recipient.
5. Email sending statistics: Postmarkapp's API allows you to extract data on the number of emails sent, the number of emails delivered, and the overall success rate of email campaigns.
6. Email bounce information: The API provides data on the reasons for email bounces, including hard bounces (permanent delivery failures) and soft bounces (temporary delivery failures).
7. Email spam complaint data: The API allows you to extract data on the number of spam complaints received for each email campaign, as well as the email addresses of users who marked emails as spam.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
1. Email delivery status: The Postmarkapp API allows you to extract data on the delivery status of emails sent through the platform. This includes information on whether an email was successfully delivered, bounced, or marked as spam.
2. Email open and click rates: The API also provides data on the number of times an email was opened and the number of clicks on links within the email.
3. Email content: You can extract the content of emails sent through Postmarkapp's API, including the subject line, body text, and any attachments.
4. Email recipient information: The API provides data on the email addresses of recipients, as well as any custom metadata associated with each recipient.
5. Email sending statistics: Postmarkapp's API allows you to extract data on the number of emails sent, the number of emails delivered, and the overall success rate of email campaigns.
6. Email bounce information: The API provides data on the reasons for email bounces, including hard bounces (permanent delivery failures) and soft bounces (temporary delivery failures).
7. Email spam complaint data: The API allows you to extract data on the number of spam complaints received for each email campaign, as well as the email addresses of users who marked emails as spam.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
1. Email delivery status: The Postmarkapp API allows you to extract data on the delivery status of emails sent through the platform. This includes information on whether an email was successfully delivered, bounced, or marked as spam.
2. Email open and click rates: The API also provides data on the number of times an email was opened and the number of clicks on links within the email.
3. Email content: You can extract the content of emails sent through Postmarkapp's API, including the subject line, body text, and any attachments.
4. Email recipient information: The API provides data on the email addresses of recipients, as well as any custom metadata associated with each recipient.
5. Email sending statistics: Postmarkapp's API allows you to extract data on the number of emails sent, the number of emails delivered, and the overall success rate of email campaigns.
6. Email bounce information: The API provides data on the reasons for email bounces, including hard bounces (permanent delivery failures) and soft bounces (temporary delivery failures).
7. Email spam complaint data: The API allows you to extract data on the number of spam complaints received for each email campaign, as well as the email addresses of users who marked emails as spam.
1. First, navigate to the Airbyte dashboard and click on "Sources" in the left-hand menu.
2. Click on the "Create Connection" button in the top right corner of the screen.
3. In the search bar, type "Postmarkapp" and select it from the list of available connectors.
4. Enter a name for your connection and click "Next".
5. In the "Connection Configuration" section, enter your Postmarkapp API key and server token.
6. Click "Test" to ensure that the connection is successful.
7. Once the test is successful, click "Create" to save the connection.
8. You can now use this connection to create a Postmarkapp source in Airbyte and start syncing your data.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
